Biology subjects

Ansariola, M.

Publications and source records attributed to Ansariola, M..

3 recordsLinked to original sources

bioRxiv2023

Comparative Analysis of Salinity Response Transcriptomes in Salt-Tolerant Pokkali and Susceptible IR29 Rice

Rice is a major cereal crop responsible for feeding the worlds population. To improve grain yield and quality, meet growing demand, and face the challenges posed by abiotic and biotic stress, it is imperative to explore genetic diversity in rice for candidate genes and loci that may contribute to stress tolerance. High salinity abiotic stress in the rice growth environment affects growth, yield, and quality. Therefore, we conducted a salt stress-responsive RNA-Seq-based transcriptome study of two rice (Oryza sativa) varieties, the salt-tolerant Pokkali and the salt-sensitive breeding line IR29. To identify early and late salinity response genes, we collected samples from the treated and untreated plants in this study at 1, 2, 5, 10, and 24 hours after treatment with 300 mM NaCl solution. We identified 7,209 and 6,595 salt-induced differentially expressed transcripts from Pokkali and IR29, respectively, over all time points. We identified [~]190,000 single nucleotide polymorphism (SNP) sites and [~]40,000 simple sequence repeat (SSR) sites, allowing analysis of their consequences on genetic diversity, transcript structure, gene function, and differential expression. We identified and validated the polymorphic SSRs in the differentially expressed salt-responsive genes Respiratory Burst Oxidase Homolog B (RBOHB) and Rice Salt Sensitive 1 (RSS1) that underly nearby salt tolerance QTLs. This study provides insight into transcriptional programming during salt stress, evidence for improving Oryza genome annotations, and reveals SNP and SSR sites associated with differential gene expression and potential gene function.

plant biology↗

bioRxiv2020

Accurate transcription start sites enable mining for the cis-regulatory determinants of tissue specific gene expression

Across tissues, gene expression is regulated by a combination of determinants, including the binding of transcription factors (TFs), along with other aspects of cellular state. Recent studies emphasize the importance of both genetic and epigenetic states - TF binding sites and binding site chromatin accessibility have emerged as potentially causal determinants of tissue specificity. To investigate the relative contributions of these determinants, we constructed three genome-scale datasets for both root and shoot tissues of the same Arabidopsis thaliana plants: TSS-seq data to identify Transcription Start Sites, OC-seq data to identify regions of Open Chromatin, and RNA-seq data to assess gene expression levels. For genes that are differentially expressed between root and shoot, we constructed a machine learning model predicting tissue of expression from chromatin accessibility and TF binding information upstream of TSS locations. The resulting model was highly accurate (over 90% auROC and auPRC), and our analysis of model contributions (feature weights) strongly suggests that patterns of TF binding sites within [~]500 nt TSS-proximal regions are predominant explainers of tissue of expression in most cases. Thus, in plants, cis-regulatory control of tissue-specific gene expression appears to be primarily determined by TSS-proximal sequences, and rarely by distal enhancer-like accessible chromatin regions. This study highlights the exciting future possibility of a native TF site-based design process for the tissue-specific targeting of plant gene promoters.

genomics↗

bioRxiv2017

IndeCut evaluates performance of network motif discovery algorithms

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no sound practical method to numerically evaluate whether any network motif discovery algorithm performs as intended--thus it was not possible to assess the validity of resulting network motifs. In this work, we present IndeCut, the first and only method that allows characterization of network motif finding algorithm performance on any network of interest. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut estimates the minimally required number of samples that each network motif discovery tool needs in order to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the most accurate network motif discovery tool for their network of interest among many available options. IndeCut is an open source software package and is available at https://github.com/megrawlab/IndeCut.

systems biology↗